In optical transmission systems including, for example, in optical wavelength division-multiplex systems (WDM systems), the problem may arise of having to monitor the transmission quality, in order to guarantee a certain (quality of service—QoS) and to be able to detect slow system degradations. Transparent, optical wavelength division-multiplex systems may be increasingly used, perhaps because they are believed to significantly increase the capacity and flexibility of today's information and telecommunications networks. Not only is an optical signal of a single wavelength transmitted via an optical fiber, but, by employing a plurality of wavelengths, a plurality of mutually independent optical channels may be made available.
Optical wavelength division-multiplex networks are transparent, analog transmission systems, which may be used for transmitting digital useful signals, and for implementing different telecommunications services. The transparency involves selecting the data rates and the format for each optical channel of a wavelength division-multiplex system independently of one another. This additionally acquired flexibility may be used to accommodate the demands of customers and to facilitate the integration of new services.
It is believed that the undefined data format may pose a serious problem in transparent networks.
The bit error rate (BER) may be considered in assessing the quality of service of a digital signal in the transmission over an optical network. It is believed that to estimate the BER of the transmitted useful signal, specific overhead bytes of the selected transmission format (e.g., SDH, ATM, etc.) are analyzed. It is believed that this method cannot be used in transparent optical systems, where the data format is “a priori” not defined. Moreover, the evaluation of the BER does not appear to permit any conclusions to be drawn with respect to the cause of a possibly occurring signal degradation. If merely the eye diagram of the received data signal is evaluated in order to assess the signal quality, then it is believed that this method requires the bit timing of the signal to be evaluated as well. Electronically acquiring the bit timing is allowable with a justifiable outlay or reasonable expenditure for fixed data rates known to the system to be evaluated. This ancillary condition or constraint may restrict the transparency of optical transport networks (WDM networks).
The reference “Application of Amplitude Histograms for Quality of Service Measurements of Optical Channels and Fault Identification,” by K. Mueller et al., ECOC 98, Sep. 20–24, 1998, Madrid, Spain, pages 707–708, discusses a method for characterizing optical transmission channels which provides for evaluating amplitude histograms. It is believed that these are acquired in that the optical signal is detected by a photodiode, which, in turn, emits an electric signal that is sampled asynchronously. The amplitude histograms may enable conclusions to be drawn with respect, for example, to the extent and the cause of slow degradations in the transmission quality.
The reference Patents of Japan, vol. 1998, no. 14, JP 10 23 92 14 A, Sep. 11, 1998, discusses a method for calculating the loss in the transition region between two optical waveguides (connection loss) for an operational wavelength. It is believed that the calculation may be carried out using a neural network, which undergoes a training until the difference between the output signal from the neural network and a training signal exceeds a specific value.
The reference “Optical Signal Quality Monitoring Method Based on Optical Sampling,” I. Shake et al., Electronics Letters, vol. 34, no. 22, Oct. 29, 1998, pages 2152–2154, discusses a method for monitoring the average Q-factor of an optical signal in an optical transmission system, amplitude histograms of optical signals being measured. From this, it is believed that information is derived about the signal-to-noise ratio of a digital signal.
The reference “Training Techniques for Neural Network Applications in ATM,” Atsushi Hiratsu, IEEE Communications Magazine, IEEE Service Center, Piscataway, N.Y., U.S.A., no. 10, vol. 33, Oct. 1, 1995, pages 58, 63–67, discusses the training of neural networks.